End-to-end Driving via Conditional Imitation Learning

Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles. However, driving policies trained via imitation learning cannot be controlled at test time. A vehicle trained end-to-end to imitate an expert cannot be guided to take a specific turn at an upcoming intersection. This limits the utility of such systems. We propose to condition imitation learning on high-level command input. At test time, the learned driving policy functions as a chauffeur that handles sensorimotor coordination but continues to respond to navigational commands. We evaluate different architectures for conditional imitation learning in vision-based driving. We conduct experiments in realistic three-dimensional simulations of urban driving and on a 1/5 scale robotic truck that is trained to drive in a residential area. Both systems drive based on visual input yet remain responsive to high-level navigational commands. The supplementary video can be viewed at https://youtu.be/cFtnflNe5fMComment: Published at the International Conference on Robotics and Automation (ICRA), 201

New Proposed Mechanism of Actin-Polymerization-Driven Motility

We present the first numerical simulation of actin-driven propulsion by elastic filaments. Specifically, we use a Brownian dynamics formulation of the dendritic nucleation model of actin-driven propulsion. We show that the model leads to a self-assembled network that exerts forces on a disk and pushes it with an average speed. This simulation approach is the first to observe a speed that varies non-monotonically with the concentration of branching proteins (Arp2/3), capping protein and depolymerization rate (ADF), in accord with experimental observations. Our results suggest a new interpretation of the origin of motility that can be tested readily by experiment.Comment: 31 pages, 5 figure

Role of electrostatic forces in cluster formation in a dry ionomer

This simulation study investigates the dependence of the structure of dry Nafion$^{\tiny\textregistered}$-like ionomers on the electrostatic interactions between the components of the molecules. In order to speed equilibration, a procedure was adopted which involved detaching the side chains from the backbone and cutting the backbone into segments, and then reassembling the macromolecule by means of a strong imposed attractive force between the cut ends of the backbone, and between the non-ionic ends of the side chains and the midpoints of the backbone segments. Parameters varied in this study include the dielectric constant, the free volume, side-chain length, and strength of head-group interactions. A series of coarse-grained mesoscale simulations shows the morphlogy to depend sensitively on the ratio of the strength of the dipole-dipole interactions between the side-chain acidic end groups to the strength of the other electrostatic components of the Hamiltonian. Examples of the two differing morphologies proposed by Gierke and by Gebel emerge from our simulations.Comment: 39 pages, 18 figures, accepted for publicatio

A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles

Funding Agency: 10.13039/100016335-Jaguar Land Rover 10.13039/501100000266-U.K. Engineering and Physical Sciences Research Council (EPSRC) (Grant Number: EP/N01300X/1) jointly funded Towards Autonomy: Smart and Connected Control (TASCC) ProgramPeer reviewedPostprin

On Offline Evaluation of Vision-based Driving Models

Autonomous driving models should ideally be evaluated by deploying them on a fleet of physical vehicles in the real world. Unfortunately, this approach is not practical for the vast majority of researchers. An attractive alternative is to evaluate models offline, on a pre-collected validation dataset with ground truth annotation. In this paper, we investigate the relation between various online and offline metrics for evaluation of autonomous driving models. We find that offline prediction error is not necessarily correlated with driving quality, and two models with identical prediction error can differ dramatically in their driving performance. We show that the correlation of offline evaluation with driving quality can be significantly improved by selecting an appropriate validation dataset and suitable offline metrics. The supplementary video can be viewed at https://www.youtube.com/watch?v=P8K8Z-iF0cYComment: Published at the ECCV 2018 conferenc